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Great progress has been made in quantum computing in recent years, providing opportunities to overcome computation resource poverty in many scientific computations like computational fluid dynamics (CFD). In this work, efforts are made to exploit quantum potentialities in CFD, and a hybrid classical and quantum computing CFD framework is proposed to release the power of current quantum computing. In this framework, the traditional CFD solvers are coupled with quantum linear algebra libraries in weak form to achieve collaborative computation between classical and quantum computing. The quantum linear solver provides high-precision solutions and scalable problem sizes for linear systems and is designed to be easily callable for solving linear algebra systems similar to classical linear libraries, thus enabling seamless integration into existing CFD solvers. Some typical cases are performed to validate the feasibility of the proposed framework and the correctness of quantum linear algorithms in CFD., Comment: 18 pages, 16 figures

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science., Comment: 31 pages, 10 figures

True random numbers are essential for scientific research and various engineering problems. Their generation, however, depends on a reliable entropy source. Here, we present true random number generation using the conductance noise probed from structurally metastable 1T' MoTe2 prepared via electrochemical exfoliation. The noise, fitting a Poisson process, is a robust entropy source capable of remaining stable even at 15 K. Noise spectral density and statistical time-lag suggest the noise originates from the random polarization of the ferroelectric dipoles in 1T' MoTe2. Using a simple circuit, the noise allows true random number generation, enabling their use as the seed for high-throughput secure random number generation over 1 Mbit/s, appealing for applications such as cryptography where secure data protection has now become severe. Particularly, we demonstrate safeguarding key biometric information in neural networks using the random numbers, proving a critical data privacy measure in big data and artificial intelligence.

Deep learning models have become a powerful tool in knee angle estimation for lower limb prostheses, owing to their adaptability across various gait phases and locomotion modes. Current methods utilize Multi-Layer Perceptrons (MLP), Long-Short Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), predominantly analyzing motion information from the thigh. Contrary to these approaches, our study introduces a holistic perspective by integrating whole-body movements as inputs. We propose a transformer-based probabilistic framework, termed the Angle Estimation Probabilistic Model (AEPM), that offers precise angle estimations across extensive scenarios beyond walking. AEPM achieves an overall RMSE of 6.70 degrees, with an RMSE of 3.45 degrees in walking scenarios. Compared to the state of the art, AEPM has improved the prediction accuracy for walking by 11.31%. Our method can achieve seamless adaptation between different locomotion modes. Also, this model can be utilized to analyze the synergy between the knee and other joints. We reveal that the whole body movement has valuable information for knee movement, which can provide insights into designing sensors for prostheses. The code is available at https://github.com/penway/Beyond-Gait-AEPM., Comment: 8 pages, 6 figures, This work has been submitted to the IEEE-RAL for possible publication

With the evolution of integrated sensing and communication (ISAC) technology, a growing number of devices go beyond conventional communication functions with sensing abilities. Therefore, future networks are divinable to encounter new privacy concerns on sensing, such as the exposure of position information to unintended receivers. In contrast to traditional privacy preserving schemes aiming to prevent eavesdropping, this contribution conceives a novel beamforming design toward sensing resistance (SR). Specifically, we expect to guarantee the communication quality while masking the real direction of the SR transmitter during the communication. To evaluate the SR performance, a metric termed angular-domain peak-to-average ratio (ADPAR) is first defined and analyzed. Then, we resort to the null-space technique to conceal the real direction, hence to convert the optimization problem to a more tractable form. Moreover, semidefinite relaxation along with index optimization is further utilized to obtain the optimal beamformer. Finally, simulation results demonstrate the feasibility of the proposed SR-oriented beamforming design toward privacy protection from ISAC receivers., Comment: Accepted for presentation at WS29 ICC 2024 Workshop - ISAC6G

Reservoir computing is a recurrent neural network that has been applied across various domains in machine learning. The implementation of reservoir computing, however, often demands heavy computations for activating the reservoir. Configuring physical reservoir networks and harnessing the nonlinearity from the underlying devices for activation is an emergent solution to address the computational challenge. Herein, we analyze the feasibility of employing the nonlinearity from solution-processed molybdenum disulfide (MoS2) devices for reservoir activation. The devices, fabricated using liquid-phase exfoliated MoS2, exhibit a high-order nonlinearity achieved by Stark modulation of the MoS2 material. We demonstrate that this nonlinearity can be fitted and employed as the activation function to facilitate reservoir computing implementation. Notably, owing to the high-order nonlinearity, the network exhibits long-term synchronization and robust generalization abilities for approximating complex dynamical systems. Given the remarkable reservoir activation capability, coupled with the scalability of the device fabrication, our findings open the possibility for the physical realization of lightweight, efficient reservoir computing for, for instance, signal classification, motion tracking, and pattern recognition of complex time series as well as secure cryptography. As an example, we show the network can be appointed to generate chaotic random numbers for secure data encryption.

Molybdenum disulfide (MoS2) has drawn great interest for tunable photonics and optoelectronics advancement. Its solution processing, though scalable, results in randomly networked ensembles of discrete nanosheets with compromised properties for tunable device fabrication. Here, we show via density-functional theory calculations that the electronic structure of the individual solution-processed nanosheets can be modulated by external electric fields collectively. Particularly, the nanosheets can form Stark ladders, leading to variations in the underlying optical transition processes and thus, tunable macroscopic optical properties of the ensembles. We experimentally confirm the macroscopic electro-optical modulation employing solution-processed thin-films of MoS2 and ferroelectric P(VDF-TrFE), and prove that the localized polarization fields of P(VDF-TrFE) can modulate the optical properties of MoS2, specifically, the optical absorption and photoluminescence on a macroscopic scale. Given the scalability of solution processing, our results underpin the potential of electro-optical modulation of solution-processed MoS2 for scalable tunable photonics and optoelectronics. As an illustrative example, we successfully demonstrate solution-processed electro-absorption modulators., Comment: Manuscript 14 pages, 5 figures. Supplementary Materials 10 pages 5 figures

Impedance-based control represents a prevalent strategy in the powered trans femoral prostheses because of its ability to reproduce natural walking. However, most existing studies have developed impedance-based prosthesis controllers for specific tasks, while creating a task-adaptive controller for variable-task walking continues to be a significant challenge. This article proposes a task-adaptive quasi-stiffness control framework for powered prostheses that generalizes across various walking tasks, including the torque-angle relationship reconstruction part and the quasi-stiffness controller design part. A Gaussian Process Regression model is introduced to predict the target features of the human joints angle and torque in a new task. Subsequently, a Kernel Movement Primitives is employed to reconstruct the torque-angle relationship of the new task from multiple human reference trajectories and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speeds and inclines walking tasks. Notably, the proposed framework not only aligns with but frequently surpasses the performance of a benchmark finite state machine impedance controller without necessitating manual impedance tuning and has the potential to expand to variable walking tasks in daily life for the trans-femoral amputees., Comment: 9 pages, 11 figures. This work has been submitted to the IEEE-TCDS for possible publication

The Electron-Ion Collider~(EIC), a forthcoming powerful high-luminosity facility, represents an exciting opportunity to explore new physics. In this article, we study the potential of the EIC to probe the coupling between axion-like particles~(ALPs) and photons in coherent scattering. The ALPs can be produced via photon fusion and decay back to two photons inside the EIC detector. In a prompt-decay search, we find that the EIC can set the most stringent bound for $m_a \lesssim 20\,\GeV$ and probe the effective scales $\Lambda \lesssim 10^{5}\,$GeV. In a displaced-vertex search, which requires adopting an EM calorimeter technology that provides directionality, the EIC could probe ALPs with $m_a \lesssim 1\,\GeV$ at effective scales $\Lambda \lesssim 10^{7}\,\GeV$. Combining the two search strategies, the EIC can probe a significant portion of unexplored parameter space in the $0.2 < m_a <20\,\GeV$ mass range., Comment: 23 pages, 9 figures

Solution-processed two-dimensional (2D) materials hold promise for their scalable applications. However, the random, fragmented nature of the solution-processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, we report conduction modulation of the solution-processed 2D materials via the Stark effect. Using liquid-phase exfoliated molybdenum disulfide (MoS2) as an example, we demonstrate nonlinear conduction modulation with a switching ratio of >105 by the local fields from the interfacial ferroelectric P(VDF-TrFE). Through density-functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, we understand the modulation arises from a charge redistribution in the solution-processed MoS2. Beyond MoS2, we show the modulation may be viable for the other solution-processed 2D materials and low-dimensional materials. The effective modulation can open their electronic device applications.

Co-movement pattern mining from GPS trajectories has been an intriguing subject in spatial-temporal data mining. In this paper, we extend this research line by migrating the data source from GPS sensors to surveillance cameras, and presenting the first investigation into co-movement pattern mining from videos. We formulate the new problem, re-define the spatial-temporal proximity constraints from cameras deployed in a road network, and theoretically prove its hardness. Due to the lack of readily applicable solutions, we adapt existing techniques and propose two competitive baselines using Apriori-based enumerator and CMC algorithm, respectively. As the principal technical contributions, we introduce a novel index called temporal-cluster suffix tree (TCS-tree), which performs two-level temporal clustering within each camera and constructs a suffix tree from the resulting clusters. Moreover, we present a sequence-ahead pruning framework based on TCS-tree, which allows for the simultaneous leverage of all pattern constraints to filter candidate paths. Finally, to reduce verification cost on the candidate paths, we propose a sliding-window based co-movement pattern enumeration strategy and a hashing-based dominance eliminator, both of which are effective in avoiding redundant operations. We conduct extensive experiments for scalability and effectiveness analysis. Our results validate the efficiency of the proposed index and mining algorithm, which runs remarkably faster than the two baseline methods. Additionally, we construct a video database with 1169 cameras and perform an end-to-end pipeline analysis to study the performance gap between GPS-driven and video-driven methods. Our results demonstrate that the derived patterns from the video-driven approach are similar to those derived from groundtruth trajectories, providing evidence of its effectiveness.

The bootstrap approach (demanding consistency conditions to scattering amplitudes) has shown to be quite powerful to tightly constrain gauge theories at large $N_c$. We extend previous analysis to scattering amplitudes involving pions and external gauge bosons. These amplitudes allow us to access the chiral anomaly and connect low-energy physical quantities to UV properties of the theory. In particular, we are able to obtain an analytic bound on the chiral anomaly coefficient as a function of the pion dipole polarizabilities. This bound can be useful for holographic models whose dual UV completions are not known, and provide a consistency condition to lattice simulations., Comment: 20 pages, 1 figure. v2: bound on the anomaly generalised and typos corrected

In quantum information transformation and quantum computation, the most critical issues are security and accuracy. These features, therefore, stimulate research on quantum state characterization. A characterization tool, Quantum state tomography, reconstructs the density matrix of an unknown quantum state. Theoretically, reconstructing an unknown state using this method can be arbitrarily accurate. However, this is less practical owing to the huge burden of measurements and data processing for large numbers of qubits. Even comprising an efficient estimator and a precise algorithm, an optimal tomographic framework can also be overburdened owing to the exponential growth of the measurements. Moreover, the consequential postprocessing of huge amounts of data challenges the capacity of computers. Thus, it is crucial to build an efficient framework that requires fewer measurements but yields an expected accuracy. To this end, we built a tomography schema by which only a few Pauli measurements enable an accurate tomographic reconstruction. Subsequently, this schema was verified as efficient and accurate through numerical simulations on the tomography of multi-qubit quantum states. Furthermore, this schema was proven to be robust through numerical simulations on a noisy superconducting qubit system. Therefore, the tomography schema paves an alternatively effective way to reconstruct the density matrix of a quantum state owing to its efficiency and accuracy, which are essential for quantum state tomography.

We derive the contact terms contributing to the four-point amplitudes of the standard-model particles, keeping terms with up to quartic energy growth. Imposing just the unbroken low-energy symmetry, and treating the electroweak gauge bosons and the Higgs as independent degrees of freedom, we obtain the most general four-point contact-term amplitudes, corresponding to the HEFT framework. The contact terms are spanned by a basis of Stripped Contact Terms (SCTs), which carry the polarization information, multiplied by polynomials in the Mandelstam invariants. For terms with quadratic energy growth, we also derive the low-energy SMEFT predictions, via on-shell Higgsing of the massless SMEFT contact terms. We discuss several aspects of bottom-up versus top-down on-shell derivations of the HEFT and SMEFT amplitudes, highlighting in particular the simple counting of HEFT dimensions in the on-shell approach and the transparent relation between perturbative unitarity and gauge-invariance in the little-group covariant massive spinor formalism. Our results provide a formulation of EFT analyses directly in terms of observable quantities. For terms with quadratic energy growth, we also provide the mapping to the Warsaw basis., Comment: 38 pages, 1 figure

We present the explicit construction of the effective field theory (EFT) of standard model mass eigenstates. The EFT, which is invariant under $U(1)_{\text{e.m.}}\times SU(3)_c$, is constructed based on the on-shell method and Young Tableau technique. This EFT serves as a new formulation of the Higgs EFT (HEFT), which can describe the infrared effects of new physics at the electroweak symmetry-breaking phase with greater conciseness. The current HEFT operator basis has a clear physical interpretation, making it more accessible for research in phenomenology. A complete list of HEFT operator bases for any-point vertices up to any dimension could be provided, and three- and four-point bases are provided as examples. Additionally, this framework realized as a Mathematica program can be used to construct the EFT of any type of dark matter or particles with any spin.

Anomalous heat transfer (AHT), a process by which heat spontaneously flows from a cold system into a hot one, superficially contradicts the Clausius statement of the second law of thermodynamics. Here we provide a full classification of mechanisms of the AHT in nonequilibrium quantum systems from a quantum-information perspective. For initial states in local equilibrium, we find that the AHT can arise from three resources: initial correlation, intrasystem interaction, and intrasystem temperature inhomogeneity. In particular, for qubit systems, we prove that initial quantum coherence is necessary for AHT if the intersystem interactions are limited to the two-body type. We explicitly show the AHT dominated by each of the mechanisms in a three-qubit system. Our classification scheme may offer a guideline for developing high-efficiency quantum heat pump., Comment: 10 pages, 6 figures

Accurate control of qubits is the central requirement for building functional quantum processors. For the current superconducting quantum processor, high-fidelity control of qubits is mainly based on independently calibrated microwave pulses, which could differ from each other in frequencies, amplitudes, and phases. With this control strategy, the needed physical source could be challenging, especially when scaling up to large-scale quantum processors is considered. Inspired by Kane's proposal for spin-based quantum computing, here, we explore theoretically the possibility of baseband flux control of superconducting qubits with only shared and always-on microwave drives. In our strategy, qubits are by default far detuned from the drive during system idle periods, qubit readout and baseband flux-controlled two-qubit gates can thus be realized with minimal impacts from the always-on drive. By contrast, during working periods, qubits are tuned on resonance with the drive and single-qubit gates can be realized. Therefore, universal qubit control can be achieved with only baseband flux pulses and always-on shared microwave drives. We apply this strategy to the qubit architecture where tunable qubits are coupled via a tunable coupler, and the analysis shows that high-fidelity qubit control is possible. Besides, the baseband control strategy needs fewer physical resources, such as control electronics and cooling power in cryogenic systems, than that of microwave control. More importantly, the flexibility of baseband flux control could be employed for addressing the non-uniformity issue of superconducting qubits, potentially allowing the realization of multiplexing and cross-bar technologies and thus controlling large numbers of qubits with fewer control lines. We thus expect that baseband control with shared microwave drives can help build large-scale superconducting quantum processors., Comment: 16 pages, 16 figures

Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud. To train our model without the time consuming and tedious data labeling process, we first generate synthetic primitives for the basic appearance of target lines, and build an iterative line auto-labeling process to gradually refine line labels on real LiDAR scans. Our segmentation model can extract lines under arbitrary scale perturbations, and we use shared EdgeConv encoder layers to train the two segmentation and descriptor heads jointly. Base on the model, we can build a highly-available global registration module for point cloud registration, in conditions without initial transformation hints. Experiments have demonstrated that our line-based registration method is highly competitive to state-of-the-art point-based approaches. Our code is available at https://github.com/zxrzju/SuperLine3D.git., Comment: 17 pages, ECCV 2022 Accepted

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this paper, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis.